Hello, everyone. Welcome to the course of machine learning with Python. In this video, we shall learn about detailed cost structure of this course. This is the broad outline of the course that we'll be covering here. In introduction, we shall learn introduction to machine learning, deep learning, AI and data science, the applications of this domain and job perspective, the different kinds of learning supervised unsupervised learning and machine learning processes. In the second model, we'll be learning introduction to Python for data science, where we learn how to install Anaconda and Jupiter notebook.

The basics of Jupiter notebook. Then will be familiar also with Python packages like NumPy, matplotlib, pandas, etc. So NumPy is used for numerical computation that properly for visualization and plotting and pandas for data analysis. The next chapter will be learning about statistics and exploratory data analysis. So here we'll be covering basics of probability theory, then understanding different types of data, examining distribution of the variables, examining relationships among the variables, exploratory data analysis using Python. In the next chapter, we'll be learning about regression analysis.

There, we should learn about linear regression model and hypothesis peameal, dictation on binary data, multivariate rotation, polynomial regression, and Python implementation of gradient descent algorithm for regression, and using in Python libraries for solving linear regression problems. In the next chapter, we'll be learning about logistic regression logistic regression For binary classification problem, the logistic regression for multi class classification problem Python implementation of gradient descent update route for logistic regression using Python built in library for logistic regression problem. Next, we shall learn about other classification algorithm such as K nearest neighbor classifier name based classifier, decision tree classifier, support vector machine classifier, random forest classifier, we will use Python inbuilt libraries to solve classification problems, using our music classification algorithms will also implement our own k nearest neighbor classifier. In dimensionality reduction, we shall learn about high dimensionality in data set and its problem which is called the curse of dimensionality.

Then we shall learn about feature selection and feature extraction techniques. Next, we'll be looking a little bit into linear algebra. And next we shall learn about principal component analysis and implementation of principal component analysis in the next chapter, which is called unsupervised learning. We'll be learning about k means clustering algorithms and each limitation. The evolution of K means clustering algorithm in Python, then hierarchical clustering and implementation of hierarchical clustering. Next Chapter would be artificial neural network, where we'll be learning about perceptron and its deep learning and its limitations.

This multi layer perceptron or MLP, and its architecture. Then learning rule of artificial neural network which is back propagation learning algorithm and in last week, Building one multi layer perceptron in Python. So, this is our core structure overall. I hope you will enjoy this course. All the best and thank you